Alternatives to Mosquitto logo

Alternatives to Mosquitto

ActiveMQ, Mosca, EMQ, VerneMQ, and RabbitMQ are the most popular alternatives and competitors to Mosquitto.
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What is Mosquitto and what are its top alternatives?

It is lightweight and is suitable for use on all devices from low power single board computers to full servers.. The MQTT protocol provides a lightweight method of carrying out messaging using a publish/subscribe model. This makes it suitable for Internet of Things messaging such as with low power sensors or mobile devices such as phones, embedded computers or microcontrollers.
Mosquitto is a tool in the Message Queue category of a tech stack.
Mosquitto is an open source tool with 3.4K GitHub stars and 1.2K GitHub forks. Here’s a link to Mosquitto's open source repository on GitHub
Top Alternatives

Mosquitto alternatives & related posts

related ActiveMQ posts

Naushad Warsi
Naushad Warsi
software developer at klingelnberg · | 1 upvotes · 130.9K views
ActiveMQ
ActiveMQ
RabbitMQ
RabbitMQ

I use ActiveMQ because RabbitMQ have stopped giving the support for AMQP 1.0 or above version and the earlier version of AMQP doesn't give the functionality to support OAuth.

If OAuth is not required and we can go with AMQP 0.9 then i still recommend rabbitMq.

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Mosca logo

Mosca

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A Node.js MQTT broker
    Be the first to leave a pro
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    Mosca
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    Mosquitto
    EMQ logo

    EMQ

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    The Scalable MQTT Broker for IoT and Mobile Applications
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    EMQ
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    Mosquitto
    VerneMQ logo

    VerneMQ

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    VerneMQ is a distributed IoT/MQTT message broker.
    VerneMQ logo
    VerneMQ
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    Mosquitto

    related RabbitMQ posts

    James Cunningham
    James Cunningham
    Operations Engineer at Sentry · | 18 upvotes · 321.9K views
    atSentrySentry
    Celery
    Celery
    RabbitMQ
    RabbitMQ
    #MessageQueue

    As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.

    Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.

    #MessageQueue

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    Tim Abbott
    Tim Abbott
    Founder at Zulip · | 14 upvotes · 219.7K views
    atZulipZulip
    RabbitMQ
    RabbitMQ
    Python
    Python
    Redis
    Redis

    We've been using RabbitMQ as Zulip's queuing system since we needed a queuing system. What I like about it is that it scales really well and has good libraries for a wide range of platforms, including our own Python. So aside from getting it running, we've had to put basically 0 effort into making it scale for our needs.

    However, there's several things that could be better about it: * It's error messages are absolutely terrible; if ever one of our users ends up getting an error with RabbitMQ (even for simple things like a misconfigured hostname), they always end up needing to get help from the Zulip team, because the errors logs are just inscrutable. As an open source project, we've handled this issue by really carefully scripting the installation to be a failure-proof configuration (in this case, setting the RabbitMQ hostname to 127.0.0.1, so that no user-controlled configuration can break it). But it was a real pain to get there and the process of determining we needed to do that caused a significant amount of pain to folks installing Zulip. * The pika library for Python takes a lot of time to startup a RabbitMQ connection; this means that Zulip server restarts are more disruptive than would be ideal. * It's annoying that you need to run the rabbitmqctl management commands as root.

    But overall, I like that it has clean, clear semanstics and high scalability, and haven't been tempted to do the work to migrate to something like Redis (which has its own downsides).

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    related Kafka posts

    Eric Colson
    Eric Colson
    Chief Algorithms Officer at Stitch Fix · | 19 upvotes · 592.6K views
    atStitch FixStitch Fix
    Kafka
    Kafka
    PostgreSQL
    PostgreSQL
    Amazon S3
    Amazon S3
    Apache Spark
    Apache Spark
    Presto
    Presto
    Python
    Python
    R Language
    R Language
    PyTorch
    PyTorch
    Docker
    Docker
    Amazon EC2 Container Service
    Amazon EC2 Container Service
    #AWS
    #Etl
    #ML
    #DataScience
    #DataStack
    #Data

    The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

    Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

    At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

    For more info:

    #DataScience #DataStack #Data

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    John Kodumal
    John Kodumal
    CTO at LaunchDarkly · | 16 upvotes · 350.4K views
    atLaunchDarklyLaunchDarkly
    Amazon RDS
    Amazon RDS
    PostgreSQL
    PostgreSQL
    TimescaleDB
    TimescaleDB
    Patroni
    Patroni
    Consul
    Consul
    Amazon ElastiCache
    Amazon ElastiCache
    Amazon EC2
    Amazon EC2
    Redis
    Redis
    Amazon Kinesis
    Amazon Kinesis
    Kafka
    Kafka

    As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

    We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

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    related Amazon SQS posts

    Tim Specht
    Tim Specht
    ‎Co-Founder and CTO at Dubsmash · | 14 upvotes · 192.4K views
    atDubsmashDubsmash
    Google Analytics
    Google Analytics
    Amazon Kinesis
    Amazon Kinesis
    AWS Lambda
    AWS Lambda
    Amazon SQS
    Amazon SQS
    Google BigQuery
    Google BigQuery
    #ServerlessTaskProcessing
    #GeneralAnalytics
    #RealTimeDataProcessing
    #BigDataAsAService

    In order to accurately measure & track user behaviour on our platform we moved over quickly from the initial solution using Google Analytics to a custom-built one due to resource & pricing concerns we had.

    While this does sound complicated, it’s as easy as clients sending JSON blobs of events to Amazon Kinesis from where we use AWS Lambda & Amazon SQS to batch and process incoming events and then ingest them into Google BigQuery. Once events are stored in BigQuery (which usually only takes a second from the time the client sends the data until it’s available), we can use almost-standard-SQL to simply query for data while Google makes sure that, even with terabytes of data being scanned, query times stay in the range of seconds rather than hours. Before ingesting their data into the pipeline, our mobile clients are aggregating events internally and, once a certain threshold is reached or the app is going to the background, sending the events as a JSON blob into the stream.

    In the past we had workers running that continuously read from the stream and would validate and post-process the data and then enqueue them for other workers to write them to BigQuery. We went ahead and implemented the Lambda-based approach in such a way that Lambda functions would automatically be triggered for incoming records, pre-aggregate events, and write them back to SQS, from which we then read them, and persist the events to BigQuery. While this approach had a couple of bumps on the road, like re-triggering functions asynchronously to keep up with the stream and proper batch sizes, we finally managed to get it running in a reliable way and are very happy with this solution today.

    #ServerlessTaskProcessing #GeneralAnalytics #RealTimeDataProcessing #BigDataAsAService

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    Praveen Mooli
    Praveen Mooli
    Engineering Manager at Taylor and Francis · | 12 upvotes · 543.2K views
    MongoDB Atlas
    MongoDB Atlas
    Java
    Java
    Spring Boot
    Spring Boot
    Node.js
    Node.js
    ExpressJS
    ExpressJS
    Python
    Python
    Flask
    Flask
    Amazon Kinesis
    Amazon Kinesis
    Amazon Kinesis Firehose
    Amazon Kinesis Firehose
    Amazon SNS
    Amazon SNS
    Amazon SQS
    Amazon SQS
    AWS Lambda
    AWS Lambda
    Angular 2
    Angular 2
    RxJS
    RxJS
    GitHub
    GitHub
    Travis CI
    Travis CI
    Terraform
    Terraform
    Docker
    Docker
    Serverless
    Serverless
    Amazon RDS
    Amazon RDS
    Amazon DynamoDB
    Amazon DynamoDB
    Amazon S3
    Amazon S3
    #Backend
    #Microservices
    #Eventsourcingframework
    #Webapps
    #Devops
    #Data

    We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

    To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

    To build #Webapps we decided to use Angular 2 with RxJS

    #Devops - GitHub , Travis CI , Terraform , Docker , Serverless

    See more

    related Celery posts

    James Cunningham
    James Cunningham
    Operations Engineer at Sentry · | 18 upvotes · 321.9K views
    atSentrySentry
    Celery
    Celery
    RabbitMQ
    RabbitMQ
    #MessageQueue

    As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.

    Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.

    #MessageQueue

    See more
    Michael Mota
    Michael Mota
    CEO & Founder at AlterEstate · | 4 upvotes · 79.4K views
    atAlterEstateAlterEstate
    Celery
    Celery
    RabbitMQ
    RabbitMQ
    Django
    Django

    Automations are what makes a CRM powerful. With Celery and RabbitMQ we've been able to make powerful automations that truly works for our clients. Such as for example, automatic daily reports, reminders for their activities, important notifications regarding their client activities and actions on the website and more.

    We use Celery basically for everything that needs to be scheduled for the future, and using RabbitMQ as our Queue-broker is amazing since it fully integrates with Django and Celery storing on our database results of the tasks done so we can see if anything fails immediately.

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